Modeling and Optimization with Artificial Intelligence in Nutrition

The use of mathematical modeling and optimization in nutrition with the help artificial intelligence is indeed a trendy promising approach to data processing. With ever-increasing amount being generated field nutrition, it has become necessary develop new tools techniques process analyze these data. paper presents study on development neural-networks-based model investigate parameters related obesity predict participants’ health outcomes. Improvement performances are made (classification performance by reducing overfitting, capturing non-linear relationships, optimizing learning process). Predictions also random forest compare accuracy prediction scores two different models. dataset contains relating 200 participants weight loss program. Information collected their basic anthropometric data, as well biochemical which significant closely obesity. It important note that not always linear can vary based individual factors; so, supervised patient (before diet regime, during reaching desired weight). trained individuals features such age; gender; body mass index; attributes MCHC (Mean Corpuscular Hemoglobin Concentration), cholesterol, glucose, platelets, leukocytes, ALT (alanine aminotransferase), triglycerides, TSH (thyroid stimulating hormone), magnesium. results developed neural network show high accuracy, low training, high-precision predictions evaluation model, improved over other machine Calculations conducted Anaconda/Python. Overall, combination modeling, optimization, AI offers powerful set for analyzing processing As our understanding relationship between continues evolve, will increasingly developing personalized dietary recommendations population-level guidelines.